Writing and translation feedback tool |
Humanities |
Claudia Kaiser, Cornelia Wiedenhofer, Nadine Buchmann
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Developing and testing an AI-driven feedback tool to provide real-time, corrective, and contextualised feedback for student translations, enhancing active learning and self-reflection, with the potential for expansion to multiple languages after initial testing with English-German translations.
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Brainard the Fox |
Humanities |
Juliana Dresvina, Daniel Gerrard, Leif Dixon, Gavin Thomas
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Developing an AI-powered research companion for undergraduate History students to help navigate pre-modern period papers by guiding them to relevant resources from Oxford's vast archives, with a focus on enhancing reading lists and supporting critical engagement without enabling essay writing.
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Support for neurodiverse Humanities students |
Humanities |
Simon Park, Machilu van Bever Donker, Phillip Rothwell, Siân Grønlie
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Exploring how AI tools can enhance support for disabled and neurodiverse Humanities students by quickly generating diverse, accessible learning materials beyond lecture recordings, addressing inclusivity and reducing staff workload.
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Compsci course guide bot |
MPLS |
Jennifer Watson, Edward Crichton, Lucy Sajdler
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Using AI to create a course guide that helps Computer Science students with personalised course selection and faculty expertise, improving academic planning and resource utilisation.
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Paper to podcast project |
MSD |
Stephen Taylor, Delia O’Rourke, Damion Young, Ruth Percy
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Introducing an AI-driven solution that transforms academic papers into engaging conversational podcasts, improving accessibility, engagement, and time-efficient knowledge acquisition.
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AI-assisted annotations for Histology practicals |
MSD |
Sharmila Saran Rajendran, Helen Christian, Mary McMenami, Damion Young, Rumyana Smilevska
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The Labbot project aims to enhance inclusive learning and student engagement in medical histology lab practicals by using an AI-driven chatbot to support first-year preclinical students in addressing queries and interacting with instructors.
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Simulated patient chatbot for medical students to practice communication and reasoning |
MSD |
Sofia I R Pereira, Philip Drennan, Damion Young, Sumathi Sekaran, Suzanne Stewart, Jack Amiry, Anna B Szabo, Dimitri Gavriloff, Vedas Thakrar, Ali Hosin, James Fullerton
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An LLM-based chatbot will simulate patient interactions in an OSCE-like format, creating a low-pressure environment where medical students can practice communication, clinical reasoning, taking medication histories, and counselling patients, while receiving AI-driven feedback to enhance their clinical pharmacology skills and confidence in prescribing medications.
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AI-driven feedback loops |
MSD |
Judy Irving, Daniel Long, Kate Forrester, Matthew Hurst
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Using AI-driven feedback loops to streamline the collection, analysis, and response to student feedback, enhancing teaching and learning while reducing the time and resources required for academic and administrative processes.
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Astrophoria personalised support |
MPLS |
Christopher Patrick, Rachel Quarrell, Nicole Miranda
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Using AI to help Astrophoria Foundation Year physical sciences students create personalized learning materials and integrate AI-based tasks into their studies to boost confidence and understanding.
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Voice chatbot for language learning |
OUDCE/Humanities |
Marion Sadoux, Cornelia Wiedenhofer, Jieun Kiaer, Emine Cakir, Elizabeth Wonnacott
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Developing an AI-powered voice chatbot integrated into Canvas to help L2 learners practice spoken interaction in various languages, reducing anxiety and fostering inclusivity
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Interdisciplinary collaboration matching |
Social Sciences |
Jeremy Knox, Rebecca Eynon, Lulu Shi
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Developing and evaluate an AI system to support interdisciplinary discussions among postgraduate students by analysing their essays and generating prompts to foster productive dialogue and collaboration across diverse disciplines.
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Reproducibility of research |
MSD |
Lucy Bowes, Nicholas Yeung, Laurence Hunt, Juuso Repo
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Enhancing teaching and learning by using AI to help students critically assess the reproducibility of research studies through a collaborative process that combines AI-driven analysis, student verification, and reporting, fostering reflection on the interaction between human judgment and AI outputs. |